基于大数据分析的电力系统故障预测与诊断方法

Power System Fault Prediction and Diagnosis Based on Big Data Analysis

在现代电力系统管理中,随着电网的复杂度和规模日益增大,传统的故障检测方法已不能满足高效和实时的监控需求。基于大数据分析的方法,利用数据挖掘和机器学习技术,提供一种先进的解决方案,使电力系统的故障预测和诊断更准确、及时。这种方法能够分析和处理大量的电力系统运行数据,如电压、电流、频率等,以识别潜在的故障和异常,从而优化电网的运行稳定性和安全性。文章概述大数据技术,探讨如何利用时间序列分析和特征识别预测系统进行故障预测,描述实时故障监测系统、自动化故障预警以及系统集成的实现,并通过应用测试验证该方法的有效性和实用性。

In modern power system management,with the increasing complexity and scale of power grid,traditional fault detection methods can no longer meet the requirements of efficient and real-time monitoring.Based on the method of big data analysis,an advanced solution is provided by using data mining and machine learning technology,which makes the prediction and diagnosis of power system faults more accurate and timely.This method can analyze and process a large number of power system operation data,such as voltage,current,frequency,etc.,to identify potential faults and anomalies,so as to optimize the operation stability and security of the power grid.This paper summarizes big data technology,discusses how to use time series analysis and feature recognition prediction system to predict faults,describes the realization of real-time fault monitoring system,automatic fault early warning and system integration,and verifies the effectiveness and practicability of this method through application tests.